ANN Algorithms Comparison
Compare all approximate nearest neighbor algorithms side-by-side: HNSW, IVF-PQ, LSH, Annoy, and ScaNN. Find the best approach for your use case.
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embeddings
Explore machine learning concepts related to embeddings. Clear explanations and practical insights.
10
Concepts Found
Concepts Related to embeddings
HNSW: Hierarchical Navigable Small World
Interactive visualization of HNSW - the graph-based algorithm that powers modern vector search with logarithmic complexity.
7 min readConcept
Vector Index Structures
Explore the fundamental data structures powering vector databases: trees, graphs, hash tables, and hybrid approaches for efficient similarity search.
11 min readConcept
IVF-PQ: Inverted File with Product Quantization
Learn how IVF-PQ combines clustering and compression to enable billion-scale vector search with minimal memory footprint.
8 min readConcept
LSH: Locality Sensitive Hashing
Explore how LSH uses probabilistic hash functions to find similar vectors in sub-linear time, perfect for streaming and high-dimensional data.
8 min readConcept
Vector Quantization Techniques
Master vector compression techniques from scalar to product quantization. Learn how to reduce memory usage by 10-100× while preserving search quality.
8 min readConcept
Dense Embeddings Space Explorer
Interactive visualization of high-dimensional vector spaces, word relationships, and semantic arithmetic operations.
6 min readConcept
Matryoshka Embeddings
Matryoshka embeddings: nested representations enabling dimension reduction by simple truncation without model retraining for flexible retrieval.
8 min readConcept
Quantization Effects Simulator
Embedding quantization simulator: explore memory-accuracy trade-offs from float32 to int8 and binary representations for retrieval.
6 min readConcept
The Modality Gap
The modality gap in CLIP and vision-language models: why image and text embeddings occupy separate regions despite contrastive training.
6 min readConcept